AI Charts Its Course in Aviation, Moving More Into Cockpit

Ask anyone what they think of when the words “artificial intelligence” and aviation are combined, and it’s likely the first things they’ll mention are drones. But autonomous aircraft are only a fraction of the impact that advances in machine learning and other artificial intelligence (AI) technologies will have in aviation—the technologies’ reach could encompass nearly every aspect of the industry. Aircraft manufacturers and airlines are investing significant resources in AI technologies in applications that span from the flightdeck to the customer’s experience.

Automated systems have been part of commercial aviation for years. Thanks to the adoption of “fly-by-wire” controls and automated flight systems, machine learning and AI technology are moving into a crew-member role in the cockpit. Rather than simply reducing the workload on pilots, these systems are on the verge of becoming what amounts to another co-pilot. For example, systems originally developed for unmanned aerial vehicle (UAV) safety—such as Automatic Dependent Surveillance Broadcast (ADS-B) for traffic situational awareness—have migrated into manned aircraft cockpits. And emerging systems like the Maneuvering Characteristics Augmentation System (MCAS) are being developed to increase safety when there’s a need to compensate for aircraft handling characteristics. They use sensor data to adjust the control surfaces of an aircraft automatically, based on flight conditions.

But machine-learning systems are only as good as the data they get. There is inherent risk in handing off more of what humans do in a high-risk environment to ML or AI that few people understand. While the final investigation of the recent crash of Lion Air 610 is still underway, the details revealed so far are a strong warning of the risks of handing off too much control to autonomous systems. While catastrophic aviation accidents seldom happen as a result of a single mistake (and this was no exception), the MCAS sensors failed, maintenance failed to fully correct the issue, and the pilots had not been fully trained and informed on the function and use of the MCAS.

The lesson, reinforced at a tragic cost of 189 lives, is that the aviation industry will have to fold data quality and the care and feeding of ML and AI systems into the safety culture that commercial aviation is already renowned for. As machine learning and AI transform the role of pilots, those technologies need to be as thoroughly tested as their human counterparts and deemed at least as competent.

Major aircraft manufacturers such as Airbus are already phasing in AI. According to Airbus Vice President for AI Adam Bonnifield, the company has been working on these technologies for a long time. “Airbus is not that unfamiliar with these technologies because of our background in aviation and building systems that essentially solve some problems in autonomy,” he told Ars.

There’s plenty of data to tap regarding machine learning aboard the modern airliner: the A350 XWB, Airbus’ twin-engine wide-body aircraft introduced in 2015, has some 50,000 sensors and collects 2.5 terabytes of data daily. And AI can make use of that data in a number of ways. Airbus is working on projects that reduce the cognitive load (and the resulting cognitive fatigue) on pilots, as well as the number of pilots required to be at the controls. This means the crew can spend more time handling the overall strategy and mission of a flight and less time dealing with all the small sub-problems of piloting an aircraft.

Bonnifield explained that, while many people view autonomy in aircraft as “a binary”—either an airplane is autonomous or it isn’t—he feels differently. “It’s more of a spectrum,” he said, “where we take some of the small problems of flying a plane and try to use AI to solve them.”

One example of this is an option available on Airbus aircraft called runway overrun protection. ROPS is software that calculates aircraft approach speed and weight, and it compares the resulting physics model with the published runway length and current local weather on approach. If it detects an unsafe situation, it broadcasts the message “Runway too short!” ROPS also calculates optimal approach glide-slopes, or trajectories, for a landing approach, and it helps with taxiing, takeoff, and other aspects of flight.

Another area of AI focus at Airbus is building autonomous vehicles and air taxis designed to transport people inside urban areas. And AI could potentially be used in a passenger plane when the pilots are rendered unconscious from a fall in cabin pressure. It can add up factors and make better decisions faster under high-pressure situations than humans given the right data, creating a potential increase in safety.